IEEE transactions on pattern analysis and machine intelligence
Dec 7, 2021
Distance between sequences is structural by nature because it needs to establish the temporal alignments among the temporally correlated vectors in sequences with varying lengths. Generally, distances for sequences heavily depend on the ground metric...
IEEE transactions on pattern analysis and machine intelligence
Dec 7, 2021
The rapid development of deep neural networks (DNNs) in recent years can be attributed to the various techniques that address gradient explosion and vanishing. In order to understand the principle behind these techniques and develop new methods, plen...
IEEE transactions on pattern analysis and machine intelligence
Dec 7, 2021
Learning the similarity between images constitutes the foundation for numerous vision tasks. The common paradigm is discriminative metric learning, which seeks an embedding that separates different training classes. However, the main challenge is to ...
IEEE transactions on pattern analysis and machine intelligence
Dec 7, 2021
Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location an...
IEEE transactions on pattern analysis and machine intelligence
Dec 7, 2021
In this work, we introduce the average top- k ( AT) loss, which is the average over the k largest individual losses over a training data, as a new aggregate loss for supervised learning. We show that the AT loss is a natural generalization of the two...
IEEE transactions on pattern analysis and machine intelligence
Dec 7, 2021
Visual Question Answering (VQA) is a task to answer natural language questions tied to the content of visual images. Most recent VQA approaches usually apply attention mechanism to focus on the relevant visual objects and/or consider the relations be...
IEEE transactions on pattern analysis and machine intelligence
Dec 7, 2021
Large-scale distributed training of deep neural networks results in models with worse generalization performance as a result of the increase in the effective mini-batch size. Previous approaches attempt to address this problem by varying the learning...
Sensors (Basel, Switzerland)
Dec 7, 2021
Deep Neural Networks (DNNs) are state-of-the-art machine learning algorithms, the application of which in electrocardiographic signals is gaining importance. So far, limited studies or optimizations using DNN can be found using ECG databases. To expl...
BMJ open
Dec 7, 2021
OBJECTIVES: To evaluate the ability of a commercially available comprehensive chest radiography deep convolutional neural network (DCNN) to detect simple and tension pneumothorax, as stratified by the following subgroups: the presence of an intercost...
Computational and mathematical methods in medicine
Dec 7, 2021
In this paper, we have proposed a novel methodology based on statistical features and different machine learning algorithms. The proposed model can be divided into three main stages, namely, preprocessing, feature extraction, and classification. In t...